Development of a Maximum Specific Photosynthetic Rate Algorithm Based on Remote Sensing Data: a Case Study for the Atlantic Ocean
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
New regional empirical algorithms were developed to obtain maximum specific photosynthetic rates of phytoplankton ( $$P_{m}^{B}$$ ) in the surface layer of the Atlantic Ocean. These algorithms were based on the dependence of $$P_{m}^{B}$$ on seawater temperature. Sea Surface Temperature remote sensing data and the PANGAEA global database of photosynthesis–irradiance parameters were used to test the algorithm. In addition, the variability in $$P_{m}^{B}$$ , both spatially (from 60° S to 85° N) and seasonally, (2002–2013) was estimated. The highest $$P_{m}^{B}$$ was obtained in December in areas of deep convection and the interaction between the Labrador Current and the Gulf Stream, while minimum values were observed in the northern and equatorial–tropical parts of the ocean during the time intervals between the phytoplankton blooms (March to September–October). In addition, existing $$P_{m}^{B}$$ and $$P_{{{\text{opt}}}}^{B}$$ algorithms used in primary production models, as well as the $$P_{m}^{B}$$ algorithm developed using temperature and chlorophyll a data from AMT-29, which were then tested using the PANGAEA dataset. The results show that the new $$P_{m}^{B}$$ algorithm developed using seawater temperature data with regionally adjusted empirical coefficients correlated best with the in situ data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it